library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.2     v dplyr   1.0.6
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(Momocs)
## 
## Attaching package: 'Momocs'
## The following objects are masked from 'package:dplyr':
## 
##     arrange, combine, filter, mutate, rename, sample_frac, sample_n,
##     select, slice
## The following object is masked from 'package:tidyr':
## 
##     chop
## The following object is masked from 'package:stats':
## 
##     filter
library(geomorph)
## Loading required package: RRPP
## Loading required package: rgl
## Loading required package: Matrix
## 
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
## 
##     expand, pack, unpack
## 
## Attaching package: 'geomorph'
## The following object is masked from 'package:Momocs':
## 
##     mosquito
library(here)
## here() starts at F:/Google Drive/Lectures/2021 BYU Digital Archaeology

Outline analysis

This code shows how outline analysis can be conducted

ls = list.files(here("JPGs"),
                pattern = "jpg",
                full.names = T)
img = import_jpg(ls)
## Extracting 152.jpg outlines...
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## Done in 1.6 secs
out = img %>%
  Out %>%
  Momocs::coo_smooth(100) %>% 
  coo_center()
## Warning: `data_frame()` was deprecated in tibble 1.1.0.
## Please use `tibble()` instead.
out %>% panel

fac = tibble(file = ls,name = names(out)) %>% 
  separate(name,
           into = c("type","no"),
           sep = "_",
           remove = F) %>% 
  mutate_at(vars(type),factor)
## Warning: Expected 2 pieces. Additional pieces discarded in 33 rows [28, 29, 30,
## 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, ...].
out$fac = fac
out %>% panel(fac = "type", palette = col_gallus)
c = calibrate_harmonicpower_efourier(out,nb.h = 25)
# use 99% result
nh = c$minh[3]
outEFA = out %>%
  efourier(nh)
## `norm=TRUE` is used and this may be troublesome. See ?efourier
outPCA = outEFA %>% PCA
outPCA %>% plot_PCA("type")

outPCA %>% LDA("type") %>% plot_LDA()
## 18 PC retained

outPCA = out %>%
  coo_sample(100) %>%
  coo_align() %>%
  coo_rotate(1.5708) %>%
  Ldk() %>%
  fgProcrustes() %>% 
  PCA
## iteration:  1    gain: 2545300 
## iteration:  2    gain: 1.1228 
## iteration:  3    gain: 0.49334 
## iteration:  4    gain: 0.015884 
## iteration:  5    gain: 0.3369 
## iteration:  6    gain: 0.16997 
## iteration:  7    gain: 0.016626 
## iteration:  8    gain: 0.0010921 
## iteration:  9    gain: 0.0013964 
## iteration:  10   gain: 0.012359 
## iteration:  11   gain: 0.011032 
## iteration:  12   gain: 0.0004922 
## iteration:  13   gain: 0.0056553 
## iteration:  14   gain: 0.0051922 
## iteration:  15   gain: 0.0024029 
## iteration:  16   gain: 9.0429e-05 
## iteration:  17   gain: 0.0010722 
## iteration:  18   gain: 0.0011716 
## iteration:  19   gain: 0.0006391 
## iteration:  20   gain: 3.546e-05 
## iteration:  21   gain: 0.00028887 
## iteration:  22   gain: 0.00030528 
## iteration:  23   gain: 0.00016177 
## iteration:  24   gain: 1.0428e-05 
## iteration:  25   gain: 7.059e-05 
## iteration:  26   gain: 7.6441e-05 
## iteration:  27   gain: 4.1415e-05 
## iteration:  28   gain: 3.1573e-06 
## iteration:  29   gain: 1.7659e-05 
## iteration:  30   gain: 1.9338e-05 
## iteration:  31   gain: 1.0575e-05 
## iteration:  32   gain: 9.2186e-07 
## iteration:  33   gain: 4.3869e-06 
## iteration:  34   gain: 4.8773e-06 
## iteration:  35   gain: 2.7015e-06 
## iteration:  36   gain: 2.658e-07 
## iteration:  37   gain: 1.0906e-06 
## iteration:  38   gain: 1.2307e-06 
## iteration:  39   gain: 6.9001e-07 
## iteration:  40   gain: 7.5587e-08 
## iteration:  41   gain: 2.7078e-07 
## iteration:  42   gain: 3.1043e-07 
## iteration:  43   gain: 1.7623e-07 
## iteration:  44   gain: 2.1277e-08 
## iteration:  45   gain: 6.7171e-08 
## iteration:  46   gain: 7.828e-08 
## iteration:  47   gain: 4.5004e-08 
## iteration:  48   gain: 5.9376e-09 
## iteration:  49   gain: 1.6646e-08 
## iteration:  50   gain: 1.9734e-08 
## iteration:  51   gain: 1.1491e-08 
## iteration:  52   gain: 1.6453e-09 
## iteration:  53   gain: 4.1205e-09 
## iteration:  54   gain: 4.9731e-09 
## iteration:  55   gain: 2.9336e-09 
## iteration:  56   gain: 4.5293e-10 
## iteration:  57   gain: 1.0186e-09 
## iteration:  58   gain: 1.2528e-09 
## iteration:  59   gain: 7.4897e-10 
## iteration:  60   gain: 1.2415e-10 
## iteration:  61   gain: 2.5193e-10 
## iteration:  62   gain: 3.1559e-10 
## iteration:  63   gain: 1.9099e-10 
## iteration:  64   gain: 3.3651e-11
outPCA %>% plot_PCA("type")

outPCA %>% LDA("type") %>% plot_LDA()
## 25 PC retained

First step for 3D gm is to digitize the landmarks

The geomorph package can be used for this. This vignette shows us how to do it.

data("scallopPLY")
my.ply <- scallopPLY$ply
fixed.lms1 <- digit.fixed(spec = my.ply, fixed = 5)
my.ply.2 <- scallopPLY$ply
fixed.lms2 <- digit.fixed(my.ply.2, 5)
surf.pts1 <- buildtemplate(spec = my.ply,
                           fixed = fixed.lms1,
                           surface.sliders = 100)
surf.pts2 <- digitsurface(spec = my.ply.2,
                          fixed = fixed.lms2)

The above code didn’t work when I tried it on projectile points.

I got an automatic process to work with Artifact GeoMorph Toolbox 3D

library(rio)
## 
## Attaching package: 'rio'
## The following object is masked from 'package:Momocs':
## 
##     export
ls = list.files(here("COADSDataset-Ohio/"),
                pattern = "xlsx",
                full.names = T)
nms = list.files(here("COADSDataset-Ohio/"),
                pattern = "xlsx",
                full.names = F) %>% 
  gsub(".xlsx","",.)
lndmrks = map_dfr(ls,~{
  tmp = import(.x)
}) %>% as.matrix %>% 
  arrayspecs(800,3)

gpa = gpagen(lndmrks)
## 
## Performing GPA
## 
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  |                                                                            
  |==================                                                    |  25%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |======================================================================| 100%
## 
## Making projections... Finished!
summary(gpa)
## 
## Call:
## gpagen(A = lndmrks) 
## 
## 
## 
## Generalized Procrustes Analysis
## with Partial Procrustes Superimposition
## 
## 800 fixed landmarks
## 0 semilandmarks (sliders)
## 3-dimensional landmarks
## 2 GPA iterations to converge
## 
## 
## Consensus (mean) Configuration
## 
##                X             Y             Z
## 1   -0.052667917  5.266810e-03  1.061008e-04
## 2   -0.053739983  4.297422e-03 -1.616543e-04
## 3   -0.054513486  3.286917e-03 -3.389355e-04
## 4   -0.054852806  2.159561e-03 -4.498207e-04
## 5   -0.055007869  1.090648e-03 -6.438897e-04
## 6   -0.054993852 -1.924893e-04 -7.435871e-04
## 7   -0.054789913 -1.181776e-03 -7.490956e-04
## 8   -0.054302275 -2.307043e-03 -1.009035e-03
## 9   -0.053444124 -3.321908e-03 -1.033583e-03
## 10  -0.052643165 -4.062066e-03 -1.220423e-03
## 11  -0.052644780 -4.071117e-03 -1.010329e-04
## 12  -0.053445268 -3.331404e-03  3.901855e-05
## 13  -0.054303325 -2.315968e-03  8.126720e-05
## 14  -0.054790114 -1.185735e-03 -2.160385e-04
## 15  -0.054993820 -2.001568e-04  1.301940e-04
## 16  -0.055007868  1.082475e-03  2.672256e-04
## 17  -0.054852845  2.152183e-03  4.455242e-04
## 18  -0.054515380  3.279180e-03  6.662042e-04
## 19  -0.053740273  4.292219e-03  6.915429e-04
## 20  -0.052669011  5.261982e-03  9.301999e-04
## 21  -0.052438007  5.474349e-03  5.981281e-05
## 22  -0.052435976  4.398741e-03 -7.640158e-04
## 23  -0.052434241  3.322277e-03 -1.290542e-03
## 24  -0.052432701  2.244692e-03 -1.700980e-03
## 25  -0.052431311  1.166258e-03 -1.926701e-03
## 26  -0.052430113  8.662421e-05 -2.028301e-03
## 27  -0.052429128 -9.937180e-04 -1.997610e-03
## 28  -0.052428047 -2.074852e-03 -1.825031e-03
## 29  -0.052427645 -3.156443e-03 -1.647049e-03
## 30  -0.052427228 -4.242632e-03 -9.526995e-04
## 31  -0.052428170 -4.246111e-03 -3.979168e-04
## 32  -0.052430217 -3.175797e-03  7.208020e-04
## 33  -0.052432167 -2.099699e-03  1.356807e-03
## 34  -0.052434111 -1.022438e-03  1.759444e-03
## 35  -0.052436396  5.644546e-05  2.105635e-03
## 36  -0.052437923  1.135354e-03  2.328477e-03
## 37  -0.052438476  2.216326e-03  2.300809e-03
## 38  -0.052439396  3.298152e-03  2.173263e-03
## 39  -0.052439507  4.380354e-03  1.784705e-03
## 40  -0.052439432  5.467525e-03  8.875673e-04
## 41  -0.049605775  7.773455e-03  2.507775e-04
## 42  -0.049602699  6.138158e-03 -9.325390e-04
## 43  -0.049599815  4.498597e-03 -1.604934e-03
## 44  -0.049597101  2.858936e-03 -2.258251e-03
## 45  -0.049594422  1.218036e-03 -2.661930e-03
## 46  -0.049591988 -4.247851e-04 -2.820690e-03
## 47  -0.049589923 -2.069855e-03 -2.711516e-03
## 48  -0.049588306 -3.716742e-03 -2.361554e-03
## 49  -0.049587323 -5.364860e-03 -1.754903e-03
## 50  -0.049586623 -7.017085e-03 -7.817531e-04
## 51  -0.049587106 -7.021854e-03 -4.546496e-05
## 52  -0.049590374 -5.385960e-03  1.092751e-03
## 53  -0.049594232 -3.747474e-03  1.882514e-03
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## 57  -0.049604921  2.819676e-03  3.238226e-03
## 58  -0.049606805  4.465546e-03  2.973247e-03
## 59  -0.049606776  6.114774e-03  2.364324e-03
## 60  -0.049606227  7.769061e-03  1.032026e-03
## 61  -0.046775548  9.552244e-03  4.853352e-04
## 62  -0.046769321  7.473030e-03 -9.820083e-04
## 63  -0.046765288  5.392543e-03 -2.042891e-03
## 64  -0.046761721  3.309140e-03 -2.741195e-03
## 65  -0.046758104  1.225329e-03 -3.216753e-03
## 66  -0.046755270 -8.598367e-04 -3.511319e-03
## 67  -0.046752658 -2.948287e-03 -3.440556e-03
## 68  -0.046749861 -5.040485e-03 -2.873406e-03
## 69  -0.046747870 -7.133440e-03 -2.192430e-03
## 70  -0.046744389 -9.230592e-03 -1.054150e-03
## 71  -0.046745306 -9.235667e-03 -4.009607e-04
## 72  -0.046752041 -7.160188e-03  1.494725e-03
## 73  -0.046756432 -5.078914e-03  2.460302e-03
## 74  -0.046760973 -2.997200e-03  3.365474e-03
## 75  -0.046765436 -9.120386e-04  3.859113e-03
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## 77  -0.046771579  3.262708e-03  3.984851e-03
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## 92  -0.043915630 -8.556118e-03  1.289069e-03
## 93  -0.043922295 -6.081202e-03  2.760445e-03
## 94  -0.043927687 -3.603278e-03  3.802440e-03
## 95  -0.043933019 -1.122910e-03  4.495131e-03
## 96  -0.043937467  1.359933e-03  4.851372e-03
## 97  -0.043940592  3.846799e-03  4.554119e-03
## 98  -0.043943523  6.335660e-03  3.989407e-03
## 99  -0.043945487  8.827944e-03  2.865322e-03
## 100 -0.043947554  1.132217e-02  1.300413e-03
## 101 -0.041115319  1.268993e-02  4.606852e-04
## 102 -0.041107967  9.898152e-03 -1.364317e-03
## 103 -0.041101345  7.105980e-03 -2.860692e-03
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## 684  0.041074771  6.877244e-03 -4.843207e-03
## 685  0.041089582  2.406264e-03 -5.119972e-03
## 686  0.041103617 -2.066879e-03 -5.028614e-03
## 687  0.041117573 -6.541235e-03 -4.647227e-03
## 688  0.041130614 -1.101895e-02 -3.945721e-03
## 689  0.041143672 -1.550037e-02 -2.701673e-03
## 690  0.041157169 -1.998625e-02 -6.091631e-04
## 691  0.041156936 -1.999295e-02  4.791091e-04
## 692  0.041136889 -1.554052e-02  3.702401e-03
## 693  0.041120473 -1.107827e-02  5.020650e-03
## 694  0.041105536 -6.606168e-03  5.071685e-03
## 695  0.041091610 -2.131273e-03  4.878546e-03
## 696  0.041077218  2.344151e-03  4.615505e-03
## 697  0.041063541  6.817960e-03  4.301625e-03
## 698  0.041050647  1.129297e-02  3.660056e-03
## 699  0.041038530  1.577420e-02  2.148915e-03
## 700  0.041027338  2.026086e-02  1.786532e-04
## 701  0.043866611  2.089829e-02 -1.080672e-03
## 702  0.043880565  1.634050e-02 -3.175918e-03
## 703  0.043894972  1.177528e-02 -3.999373e-03
## 704  0.043909835  7.208307e-03 -4.491755e-03
## 705  0.043923440  2.638123e-03 -4.667518e-03
## 706  0.043936911 -1.935533e-03 -4.615918e-03
## 707  0.043950227 -6.508633e-03 -4.180446e-03
## 708  0.043963216 -1.108417e-02 -3.608421e-03
## 709  0.043975191 -1.566366e-02 -2.579737e-03
## 710  0.043989392 -2.024790e-02 -4.726699e-04
## 711  0.043987861 -2.025326e-02  4.338079e-04
## 712  0.043968562 -1.570147e-02  3.715020e-03
## 713  0.043953518 -1.113737e-02  4.621524e-03
## 714  0.043939191 -6.566817e-03  4.610143e-03
## 715  0.043925684 -1.993115e-03  4.409168e-03
## 716  0.043912269  2.580531e-03  4.175858e-03
## 717  0.043899692  7.153312e-03  3.841420e-03
## 718  0.043887963  1.172695e-02  3.227864e-03
## 719  0.043876573  1.630706e-02  1.932311e-03
## 720  0.043864541  2.089273e-02 -2.821972e-04
## 721  0.046701823  2.044808e-02 -1.054718e-03
## 722  0.046716574  1.598966e-02 -3.135636e-03
## 723  0.046730467  1.152523e-02 -3.999726e-03
## 724  0.046743624  7.056422e-03 -4.115535e-03
## 725  0.046755932  2.584732e-03 -4.061815e-03
## 726  0.046768420 -1.888347e-03 -3.939076e-03
## 727  0.046781968 -6.360525e-03 -3.672378e-03
## 728  0.046794429 -1.083577e-02 -3.068304e-03
## 729  0.046805799 -1.531411e-02 -2.247344e-03
## 730  0.046817947 -1.979889e-02 -1.406677e-04
## 731  0.046817437 -1.980216e-02  6.291355e-04
## 732  0.046800691 -1.534724e-02  3.326172e-03
## 733  0.046785987 -1.088210e-02  4.141499e-03
## 734  0.046771557 -6.411099e-03  4.069057e-03
## 735  0.046758242 -1.938457e-03  3.943623e-03
## 736  0.046745743  2.534938e-03  3.613796e-03
## 737  0.046734232  7.007578e-03  3.303351e-03
## 738  0.046723547  1.148040e-02  2.812920e-03
## 739  0.046712610  1.595769e-02  1.814288e-03
## 740  0.046701311  2.044212e-02 -1.622515e-04
## 741  0.049537157  1.905339e-02 -1.263110e-03
## 742  0.049551383  1.493814e-02 -2.842337e-03
## 743  0.049564355  1.082126e-02 -3.451185e-03
## 744  0.049577441  6.699460e-03 -3.336332e-03
## 745  0.049589983  2.576340e-03 -3.314363e-03
## 746  0.049602302 -1.547209e-03 -3.256087e-03
## 747  0.049615651 -5.669678e-03 -2.971098e-03
## 748  0.049628886 -9.795451e-03 -2.496942e-03
## 749  0.049641328 -1.392448e-02 -1.755267e-03
## 750  0.049653328 -1.805779e-02 -6.958285e-05
## 751  0.049652882 -1.806322e-02  1.130405e-03
## 752  0.049636350 -1.395646e-02  3.285764e-03
## 753  0.049622011 -9.835219e-03  3.466920e-03
## 754  0.049606940 -5.711660e-03  3.354989e-03
## 755  0.049593565 -1.587955e-03  3.234211e-03
## 756  0.049580757  2.537872e-03  2.877167e-03
## 757  0.049568627  6.661690e-03  2.551146e-03
## 758  0.049557495  1.078495e-02  2.249271e-03
## 759  0.049546520  1.491012e-02  1.565047e-03
## 760  0.049535783  1.904838e-02 -4.244173e-04
## 761  0.052374817  1.591788e-02 -9.230404e-04
## 762  0.052385671  1.263743e-02 -2.178854e-03
## 763  0.052409049  8.374854e-03 -2.260147e-03
## 764  0.052419484  5.070302e-03 -2.217362e-03
## 765  0.052430608  1.777863e-03 -2.138220e-03
## 766  0.052440810 -1.511372e-03 -2.178896e-03
## 767  0.052452703 -4.802467e-03 -1.984901e-03
## 768  0.052464497 -8.095186e-03 -1.534018e-03
## 769  0.052474945 -1.139010e-02 -1.030532e-03
## 770  0.052483646 -1.468928e-02  6.841018e-05
## 771  0.052482596 -1.469680e-02  1.187711e-03
## 772  0.052471505 -1.141454e-02  2.587176e-03
## 773  0.052459421 -8.123980e-03  2.784289e-03
## 774  0.052446607 -4.831130e-03  2.533453e-03
## 775  0.052435702 -1.538463e-03  2.289717e-03
## 776  0.052424428  1.754547e-03  1.915297e-03
## 777  0.052413891  5.047830e-03  1.528819e-03
## 778  0.052403127  8.350974e-03  1.459998e-03
## 779  0.052381569  1.261605e-02  1.224388e-03
## 780  0.052373449  1.591213e-02  1.398613e-04
## 781  0.052537366  1.572031e-02 -9.752965e-04
## 782  0.053785456  1.250415e-02 -9.638451e-04
## 783  0.054212459  9.218357e-03 -7.285493e-04
## 784  0.054428565  4.671589e-03 -7.294579e-04
## 785  0.054600235  1.369621e-03 -3.459809e-04
## 786  0.054803385 -1.949813e-03 -2.133717e-04
## 787  0.054817612 -5.176802e-03 -5.381431e-05
## 788  0.054541350 -8.520142e-03  1.527212e-04
## 789  0.054137229 -1.159491e-02  2.320453e-04
## 790  0.052575182 -1.458291e-02  2.013420e-04
## 791  0.052574594 -1.458900e-02  1.142589e-03
## 792  0.054135569 -1.160053e-02  1.280171e-03
## 793  0.054540304 -8.526785e-03  1.329660e-03
## 794  0.054816932 -5.181950e-03  8.601824e-04
## 795  0.054802671 -1.955406e-03  4.493209e-04
## 796  0.054599639  1.364968e-03  2.522633e-04
## 797  0.054428038  4.666895e-03  6.707766e-06
## 798  0.054211914  9.212238e-03  1.552242e-04
## 799  0.053784712  1.249729e-02 -7.505829e-05
## 800  0.052535431  1.571357e-02  2.986010e-04
# plotAllSpecimens(gpa$coords)
pca = gm.prcomp(gpa$coords)
plotdf = pca$x[,1:2] %>%
  as_tibble() %>% 
  mutate(names = nms)
plotdf %>% ggplot(aes(Comp1,Comp2,label = names)) +
  geom_point()

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:rio':
## 
##     export
## The following objects are masked from 'package:Momocs':
## 
##     arrange, export, filter, mutate, rename, select, slice
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
ggplotly()